Cooperative uplink multi-base station (BS) reception has emerged as a promising technology to enhance received signal strength and improve wireless spectral efficiency. However, realizing the potential performance gains remains challenging due to substantial communication overhead among cooperative BSs and excessive delays in channel state information (CSI) feedback. This paper investigates a CSI feedback-free mechanism that leverages time-invariant physical layer parameters to facilitate cooperative BS reception within a fully-decoupled radio access network (FD-RAN). First, given the dynamically changing characteristics of the wireless environment, we employ conditional variational autoencoder (CVAE), a state-of-the-art generative artificial intelligence (GAI) approach, to generate location-specific representative channels for calculating CSI feedback-free transmission parameters. Subsequently, to maximize the throughput of user equipment (UE), a diffusion model-based deep reinforcement learning (DRL) framework is proposed for jointly selecting cooperative BS reception sets and precoding schemes, utilizing the representative channels generated by CVAE. Extensive simulations conducted on a link-level simulator demonstrate that the proposed CSI feedback-free mechanism for cooperative multi-BS reception can effectively improve spectrum efficiency by 17.3%, which provides a promising design principle for the development of sixth-generation (6G) wireless networks.

Ultra-dense low earth orbit (LEO) satellite networks (UDLSNs) present a viable solution to deliver high-speed, lowlatency Internet services. Data transmission scheduling stands as a key technology for guaranteeing high-speed Internet services. Nonetheless, data transmission scheduling in UDLSNs encounters some critical challenges, predominantly related to the complexity of network scale, resource contention and the stringency of user demands. In this paper, we focus on the typical application scenario of satellite network, namely Airborne Internet, and delve into the data delivery scheduling problem in UDLSN. We aim to maximize the number of successfully scheduled flows by jointly optimizing satellite-to-aircraft downlink subchannel access, intersatellite link path planning, and flow scheduling based on timeexpanded graphs. Given the large-scale nature of the network, we decouple the proposed problem into a downlink subchannel access problem and a flow scheduling problem. We further design the matching-based subchannel allocation (MSA) algorithm and$A^{*}$-based path planning and flow scheduling (APPFS) algorithm to solve the two subproblems, respectively. We use authentic civil aviation flight trajectories and$\mathbf{1 1, 9 2 6}$LEO satellites in the entire Starlink phase as simulation data to evaluate the effectiveness of the proposed algorithm. Simulation results validate that the proposed algorithm can satisfy stringent user latency requirements and improve the number of successfully transmitted data.

In satellite-terrestrial links, the high-speed movement of low earth orbit (LEO) satellites induces significant doppler effects. By employing orthogonal time frequency space (OTFS) modulation technology, information symbols are effectively mapped into the delay-doppler domain, significantly mitigating the adverse impacts of doppler effects on transmissions. Furthermore, the short packet is introduced into satelliteterrestrial communications to meet diverse latency requirements. Due to the limited channel coding capacity of short packets, adjusting the channel coding rate and symbol modulation order according to the channel conditions is crucial in satellite communications. However, real-time channel state information feedback is precluded by the rapid variations in satellite-terrestrial channels, the large feedback latency, and the limited resources for frequent feedback. Therefore, implementing a feedback-free transmission approach is particularly essential. In this paper, we design a feedback-free OTFS-based adaptive modulation and coding (AMC) scheme to accommodate varying communication requirements and conditions. Extensive simulations with different orbital altitudes, carrier frequencies, and packet lengths have shown that the proposed approach can be effectively adapted to LEO satellite channels, enabling a feedback-free AMC in the LEO satellite-terrestrial communications.

With the advancement of low-earth orbit (LEO) satellite technologies, the Large-Scale LEO Satellite Networks (LSLSNs) have become the cornerstone of next-generation wireless communication, delivering global coverage with low latency and massive throughput. However, the LSLSNs operate in an open space environment, where the effects of space environmental factors such as electromagnetic radiation, thermal conditions and solar flares can easily lead to regional correlated damage for satellite nodes, severely degrading network performance. To ensure stable and robust transmission, we propose a Robust and Intelligent Multipath QUIC Transmission (RIMT) method in LSLSNs. The RIMT method is deployed in multi-domain based satellite networks leveraging distributed Software-Defined Networking (SDN) technology, where satellites are divided into various autonomous domains managed by a local SDN controller for high efficiency and flexible management. To address regional correlated damage, RIMT employs pre-computed backup flows to seamlessly switch from compromised flows. Additionally, we introduce an innovative congestion control algorithm designed to maintain stable data transmission during the route switching process. We implement RIMT in the Kuiper K3 shell network, experiments show that RIMT can achieve more enhanced performance than other mechanisms.

The ultra-dense low-earth orbit (LEO) satellite networks (ULSNs) have become an important component of next-generation (6G) wireless networks, offering large-scale coverage and high-capacity service. Unlike traditional terrestrial network backbones deployed in closed, protected environments, satellite networks are exposed to highly dynamic environments, where space environment interference can severely affect channel conditions. This paper addresses the impact of sun outages, one of the most significant spatial interference factors, on satellite communication and proposes an adaptive coding and modulation scheme that dynamically adjusts the modulation and coding schemes (MCSs) based on real-time channel conditions to enhance the performance and communication quality of ULSNs. First, we model the channel environment of satellite-terrestrial microwave and inter-satellite laser links under sun outage interferences in ULSNs, which involves sun outage occurrence prediction and their interference quantification. Subsequently, to implement the ACM scheme, we use the seasonal autoregressive integrated moving average (SARIMA) algorithm combined with the bidirectional long short-term memory (BiLSTM) algorithm for time-series prediction of the channel state. Based on this, we apply the knowledge distillation-assisted proximal policy optimization (KD-PPO) algorithm to select the appropriate MCS. Simulation results show that by calculating the sun outage duration and the resulting interference, as well as predicting the channel state, the proposed KD-PPO algorithm can minimize the bit error rate (BER) and maximize the spectrum utilization (SU) in ULSNs.

Rate-splitting multiple access (RSMA) has recently gained attention in low earth orbit (LEO) satellite-terrestrial networks (LSTNs), due to its ability to provide high spectral efficiency in the context of constrained energy resources of LEO satellites. However, the impracticality of acquiring perfect real-time channel state information (CSI), due to high satellite mobility and long link delay, poses significant challenges to effective utilization of RSMA in LSTNs. To tackle this challenge, we propose a location-based robust RSMA scheme for downlink data transmission in LSTNs. First, we establish an optimization problem to minimize the power consumption of LEO satellites, while meeting user requirement on the real-time data rate violation probability. Subsequently, we transfer the probability constraints of rate violation probabilities into closed-form inequalities, by utilizing Markov inequality, Jensen’s inequality, and Cauchy-Schwarz inequality. The original problem is then transformed into a Markov decision process (MDP), and a Transformer encoder-based deep reinforcement learning (TDRL) algorithm is proposed to solve the complex problem based on the real-time locations of users and the LEO satellite. Additionally, a multi time-frame location-based training dataset generation method is proposed for the training of TDRL model, considering the mobility of LEO satellite. Simulation results demonstrate that the proposed scheme is effective in guaranteeing the rate violation probability requirement of each user, and RSMA significantly outperforms space division multiple access (SDMA) and non-orthogonal multiple access (NOMA), with TDRL achieving faster convergence than other baselines.

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms.

The booming renaissance and rapid development of ultra-dense low earth orbit (LEO) satellite networks (UD-LSNs) are envisioned to realize a giant leap forward for the future sixth generation (6G) coverage expansion, bridging digital divide for remote areas and providing continuous services for user terminals worldwide. However, the inherent dual mobility, massive access scenarios and highly overlapped coverage may trigger frequent, vast and ping-pong handovers, especially with the existing limited and fixed deployment of terrestrial mobility functional entity. To this end, by exploiting the unique opportunity of UD-LSNs, we devise a medium Earth orbit (MEO) assisted distributed hierarchical mobility management architecture (HDMMA) with flexible function configuration to adapt the high dynamic and large scale network. Subsequently, the lightweight handover procedures (LHPs) are proposed for two scenarios under the HDMMA to ensure service continuity, that is on-orbit handover and off-orbit handover. Considering the user mobility attributes and satellite available resources, the on-orbit handover introduces user aggregate to share signaling overhead, while the off-orbit handover is further classified into intra-cluster, inter-cluster and inter-group handover based on the clustering and grouping. Furthermore, we conduct theoretical analysis model on the proposed LHP in terms of signaling overhead and handover latency. Simulation results verify the handover characteristics in UD-LSNs, illustrate the superiority of our HDMMA and demonstrate the handover performance improvement of the proposed LHP.

Ultra-dense low earth orbit (LEO) satellite terrestrial networks (ULSNs) are considered as a crucial component of future six generation (6G) networks, offering ubiquitous and massive services for various applications. However, for the development of advanced physical layer technologies for ULSNs, a comprehensive link-level simulation tool that integrates up-to-date satellite communication protocols becomes paramount and is urgently needed. In this paper, we develop a versatile simulator for the link-level performance analysis of ULSNs under the prevalent digital video broadcasting (DVB) standards. We first establish a complete satellite-terrestrial microwave channel model, taking practical factors such as rain attenuation, cloud attenuation, and Doppler frequency shift into consideration. Subsequently, the whole physical layer modules tailored for satellite-terrestrial microwave communication are implemented, including diverse physical layer modulation and coding schemes (MCSs). Furthermore, we realize adaptive coding and modulation (ACM) for adaptive channel performance simulation. Finally, comparative performance analysis using the established channel model is conducted to demonstrate the effectiveness of different MCSs of DVB standards. The complete link-level performance analysis based on our self-developed simulator can advance the field of satellite-terrestrial microwave communication and provide valuable insights for further exploration of ULSNs.

As one of the most promising architecture in the evolving sixth-generation (6G) systems, ultra-dense low Earth orbit (LEO) satellite network (UD-LSN) is drawing increasing attention due to its global coverage and ubiquitous access. To ensure service continuity, mobility management with provision of seamless handover is crucial in the process of satellite and user movement. However, massive service requests and overlapped satellite coverage will result in frequent handovers and diversified options in the UD-LSN. Meanwhile, existing mobility management methods based on the terrestrial networks are difficult to make timely and effective decisions due to the limited deployments of ground stations. In light of this, we propose a two-layer grouping and clustering based mobility management architecture (GCMMA) for the UD-LSN to reduce the management complexity with supporting the flexible function configurations. Under the GCMMA, we design lightweight handover procedures for different scenarios according to the established handover model, which considers user aggregation and combines with the regularity of satellite motion. Simulation results validate the effectiveness of the proposed mechanism, which has a better performance in handover delays and signaling overheads.

With the rapid expansion of the scale of deployed low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising architecture in the sixth-generation (6G) system to implement seamless connectivity and high-speed data rate service. Especially for ultra-remote real-time services with long transmission distance and high delay requirements, the integrated network can guarantee its end-to-end service continuity. However, many challenges have been posed to the efficient resource orchestration for the service delivery, owing to the large scale, heterogeneity and high mobility of the integrated network. For each service, its data needs to go through a series of on-board processing, before being downloaded to the terrestrial network for further applications. To this end, service function chain (SFC), an ordered concatenation of network functions (NFs), is introduced to support service provision. By allocating the constituent NFs over the LTIN, we propose an efficient multiple service delivery scheme to minimize the overall delivery completion latency, while taking into account resource sharing and competition among multiple SFCs. First, we formulate the multiple SFC embedding problem as a noncooperative game that is further proved as the weighted potential game with at least one Nash equilibrium (NE). With the help of the proposed global coordination mechanism, we design two algorithms to obtain the NE. One is the best response (BR) algorithm with faster convergence, while the other is adaptive play (AP) algorithm with more capacity for best solutions. Then, the stochastic learning (SL) algorithm is proposed to adapt to network dynamics and reduce global information exchange. Finally, extensive simulations validate the convergence and effectiveness of the proposed algorithms.

Nowadays, the ultra-dense low earth orbit (LEO) satellite network has become a promising paradigm in the next generation mobile communication network. With the development of content centric communication, multicast technology also attracts much attention. In this paper, we consider the downlink multicast transmission in the ultra-dense LEO satellite network. Multiple LEO satellites provide multicast service for multiple ground user (GU) groups under their coverage, where each GU group requests the same content. To improve the multicast performance, we propose an optimal subchannel allocation and beamforming scheme to maximize the system max-min fair (MMF) capacity of GUs. By leveraging the many-to-many matching model, we obtain the optimal subchannel allocation solution, and we propose a successive convex approximation (SCA) based algorithm for the downlink beamforming in the matching process. The many-to-many matching algorithm is convergent to a stable solution after finite iterations. Simulation results show the superiority and the effectiveness of the proposed subchannel allocation and beamforming method compared with other baseline schemes.

Recently, the rapid-developed mega low earth orbit (LEO) satellite constellation has shown its great potential in cooperating with terrestrial networks to provide seamless global connectivity and high-speed data rate services. However, the heterogeneity of physical resources and diversity of service demands pose challenges for delivering service in an efficient way in the ultra-dense LEO satellite-terrestrial integrated networks (LTIN). When implementing service delivery, service data generally needs a series of on-board processing and then downloading to the terrestrial network for further applications. In this paper, we introduce service function chain (SFC), a sequence of network functions, to process the data on board and propose an efficient multiple service delivery scheme in the LTIN to minimize the total delivery completion time. Considering the heterogeneous resource sharing and competition among multiple SFCs, we formulate the problem as a noncooperative game, which is further proved as a weighted potential game. We design an improved response (IR) algorithm with fast convergence and an adaptive play (AP) algorithm to find the best Nash equilibrium (NE). Extensive simulation results validate the convergence and effectiveness of the proposed algorithms.